- Status of this document: This document is currently under development.
- Copyright: (C) Qianqian Fang (2011, 2015-2024) <q.fang at neu.edu>
- License: Apache License, Version 2.0
- Format Version: 1 (Draft 3.preview)
- Abstract:
JData is a general-purpose data interchange format aimed for portability, readability and simplicity. It utilizes the JavaScript Object Notation (JSON) [RFC4627] and binary JSON formats to store complex hierarchical data in both text and binary formats. In this specification, we define a list of JSON-compatible annotations to encode a wide range of commonly used data structures, including scalars, arrays, structures, tables, hashes, linked lists, trees and graphs, and support optional annotations for data grouping and metadata for each data element. The generated data files are compatible with JSON/binary JSON specifications and can be readily processed by most existing parsers. Advanced features such as array compression, data linking and anchoring are supported to greatly enhance portability and scalability of the generated data files.
- Introduction
- Grammar
- Data Models
- Data Annotation Keywords
- Indexing and Accessing JData
- Converting Between JData Files
- Data Referencing and Links
- Recommended File Specifiers
- Summary
Data are the digital representations of our world. Generating and processing data are essential parts of our daily lives, and form the very foundations of modern sciences, information technologies, businesses, and interactions between global societies.
Data can take many forms. Some data can be represented by simple scalars. Others have complex forms with hierarchical structures. An efficient representation of data also strongly depends upon application-specific needs. In some cases, plain text files with white-space delimited fields are sufficient, however, for performance-sensitive applications, binary formats can significantly reduce loading and processing time. The ability to store and parse complex data structures is particularly important to the scientific community.
It is a challenging task to encapsulate a wide variety of data forms within a single data interchange format. There have been many previous efforts in designing a general-purpose data storage specification. Some of them have become popular choices in one or multiple applications. Extensible Markup Language (XML), for example, is ubiquitously used as a data-exchange format, but the verbosity of the syntax, moderate complexity for parsing, impeded readability and inefficiency in expressing structured data Indicate room for improvement. Comma Separated Value (CSV), a rather simple plain-text format, is used among some applications to exchange tabular data structures (such as spreadsheets); yet, its inability to encode more complex data forms, lack of flexibility and data precision restrict it to specific applications.
The Hierarchical Data Format (HDF) is a format targeting the broad needs of the scientific communities. It has an extensible hierarchical data model with a large capacity to represent complex binary data. However, to effectively use HDF requires skillful implementation and an in-depth understanding of the complex underlying programming interfaces. For small projects with non-critical performance needs, using an advanced data format such as HDF may require additional development and maintenance efforts. Similar arguments can be made for the Common Data Format (CDF) or Network Common Data Format (netCDF) that are partly derived from HDF. In addition, the MATLAB mat-file format and Python pickle format have also been used among the research communities. However, their usage has been largely limited to the respective programming environments.
The JavaScript Object Notation (JSON) format is a text-based data format that is known for its complex data storage capability, excellent portability and human-readability. JSON has been widely adopted in modern web applications, and is becoming popular among local/native applications. The key advantages of JSON include:
- simplicity: JSON data are composed of lists of
"name":valuepairs; such simple syntax greatly eases the use and parsing of the data file; free JSON-encoders and decoders are widely available for most popular programming languages; - human-readability: the text-based nature of JSON and its clean, easy-to-read format make it intuitively readable without in-depth knowledge of the format itself;
- hierarchical data support: JSON has a tree-like data storage paradigm which has the capacity to support complex hierarchical data structures; there is no inherent data size limit imposed by the format itself;
- web-readiness: because JSON can be readily parsed by JavaScript, most JSON-encoded data files can be directly invoked (inline or loaded from remote sites) by a JavaScript based web-application.
JSON also has limitations. JSON's "value" fields are weakly-typed. They only
support strings, numbers, and Boolean types, and lack the fine-granularity
to represent various numerical types of different byte-lengths (in C-language,
for example, short, int, long int, float, double, long double) and their signs
(signed and unsigned). Because JSON is a text-based format, the size of the
data file can be significantly larger than a respective binary file and requires
additional conversion when used in an application. This introduces overhead in
both storage and processing.
The Binary JData (BJData) format was derived from the Universal Binary JSON (UBJSON) format, which is one of the binary counterparts to the JSON format. It specifically addresses the above mentioned limitations, yet adheres to a simple grammar similar to the text-based JSON. Compared to other binary JSON-like formats, such as BSON (Binary JSON, http://bson.org), CBOR (Concise Binary Object Representation, [RFC 7049], https://cbor.io) and MessagePack (https://msgpack.org), BJData and UBJSON files are "quasi-human-readable" - a unique capability that is absent from almost all other binary formats. Compared to UBJSON, BJData specification supports extended binary data types (such as unsigned integers and half-precision floating-point numbers) as well as optimized N-dimensional array format. The extended data constructs also allow a BJData file to store binary arrays larger than 4 GB in size, which is not currently possible with MessagePack (maximum data record size is limited to 4 GB) and BSON (maximum total file size is 4 GB).
With ease-of-use, superior portability and parser availability, JSON and BJData/UBJSON have the potential to serve as main-stream data storage and interchange formats for general needs, especially for the storage and interchange scientific data. A combination of JSON and its binary counterpart offers features that are not currently available within existing data storage schemes. Although they do not provide all of the advanced features found in more sophisticated formats, their greatly simplified encoding and decoding strategies permit efficient data sharing among general audiences.
JData is a specification for storing, exchanging and processing general-purpose data that are commonly encountered in the information technology (IT) industries and research communities. It has a text/UNICODE format derived from the JSON specification and a binary format derived from the BJData/UBJSON specification. JData is designed to represent commonly used data structures, including arrays, structures, trees and graphs. A round-trip conversion is defined between the text and binary versions of JData documents.
The inception of this specification started in 2011 as part of the development of the JSONLab Toolbox - a popular open-source MATLAB/GNU Octave JSON reader/writer. The majority of the annotated N-D array constructs had been implemented in the early releases of JSONLab. In 2015, the initial draft of this specification was developed in the Iso2Mesh Wiki; since 2019, the development has been migrated to Github.
The purpose of this document is to define the text and binary JData format specifications. This is achieved through defining a semantic layer over the JSON/UBJSON data storage syntax to map various types of complex data structures. Such a semantic layer includes
- a list of dedicated
"name"fields, or keywords, that define the containers of various data types that are commonly used in research, - a list of dedicated
"name"fields and formats to facilitate the grouping and organization of hierarchical data, - a list of format properties for the associated "value" field to store the specific metadata of the data points
- a set of conversion rules between the text and binary forms.
In the following sections, we will define the basic JData grammar and data models, followed by the keywords for data grouping and various data types, including scalars, N-dimensional arrays, sparse and complex arrays, structures, tables, hashes/associative arrays, trees and graphs. The expressions for these data structures in both text and binary forms are specified and exemplified, and their conversion rules are defined.
The text-based JData grammar is identical to the JSON grammar defined in [RFC4627], with the exception that JData also accepts the Concatenated JSON (CJSON) streaming format, defined in the following form:
{
"object1":{}
}
{
"object2":{}
}
...
In CJSON, multiple JSON objects can be included inside a single file or stream, separated by 0 or multiple permitted white spaces, namely
LF (U+000A), CR (U+000D), tab (U+0009), or space (U+0020)
The Binary JData (BJData) format used in this specification is based on BJData Specification (Draft 2), which is extended from the widely used UBJSON Specification (Draft 12), with the addition of the following features
- BJData supports 4 new data type markers:
[u]: uint16,[m]: uint32,[M]: uint64,[h]: float16 - BJData uses little-Endian (LE) as the default numeric (integers and floating-point numbers) data byte order as oppose to the big-Endinan (BE) byte order used by UBJSON
- BJData uses the respective IEEE 754 binary form to store +/-Infinity and NaN instead of converting to [Z]
- optimized array container header was extended to support N-dimensional packed arrays by
attaching a 1-D integer array construct following the
#(count) marker, for example
[[] [$] [type] [#] [[] [$] [nx type] [#] [ndim type] [ndim] [nx ny nz ...] [nx*ny*nz*...*sizeof(type)]
or
[[] [$] [type] [#] [[] [nx type] [nx] [ny type] [ny] [nz type] [nz] ... []] [nx*ny*nz*...*sizeof(type) ]
where ndim is the number of dimensions, and nx, ny, and nz ... are
all non-negative numbers specifying the dimensions of the N-dimensional array.
nz/ny/nz/ndim types must be one of the BJData integer types (i,U,I,u,l,m,L,M).
The binary data of the N-dimensional array is then serialized in the row-major format
(similar to C, C++, Javascript or Python) order.
As a special note, all BJData/UBJSON integer and floating-point types must be stored in the Little-Endian format, according to BJData Specification (Draft 2).
Topologically, we define different parts of a JData document using the below terminologies
- leaflet : an element with no child
- compound leaf, or "leaf": a multi-element data unit completely made of leaflets, or empty
- branch: a multi-element data unit made of both leaves and leaflets.
- node: a leaflet, a leaf or a branch
- composite node: a leaf or a branch
- root: the top-most node of a JSON object
- super-root: the top level node in a JData document containing multiple JSON/binary-JSON objects in the form of a CJSON
- named node: a node in the form of
"name":{...}or"name":[]in the case of named and index leaves and branches, respectively, or"name":valuein the case of a leaflet - indexed node: a node in the form of
{...}or[]in the case of named and index leaves and branches, respectively, or a singlevaluein the case of a leaflet - a structure: a named or index node made of named sub-nodes, represented by
{...}; a structure can be empty - an array: a named or index node made of indexed sub-nodes, represented by
[...]; an array can be empty
A leaflet can only take one of the two possible forms: value, referred to as the
"index leaflet", and a "name": value pair, referred to as the "named leaflet".
Here value should not contain any sub-element.
The topological elements of a JData document - "leaflet", "leaf", "branch", "root" and "super-root" - have specific meanings according to the definitions above.
To efficiently process the represented data, we can semantically annotate the underlying data structure and organize them for fine granularity. The data grouping annotations supported in JData include
- data record: a "meaningful" datum, either in the form of a simple data point (leaflet) or a complex structure (a composite node); a data record can be a leaflet, a leaf, or a collection of leaflets or leaves.
- dataset: a set of "logically connected" data records, such as the citation information of a paper
- data group: a group of "logically connected" datasets
- auxiliary data: nodes that store "weakly-relevant" or "irrelevant" data to the "primary" data. The auxiliary data can appear at any level in the data annotation hierarchy.
The interpretations for "meaningful datum", "logically connected data", "primary data" and "weakly-relevant or irrelevant data" are application dependent.
The data group annotations are ranked in the below order, from highest to lowest level:
super-root > root > data group >= dataset >= data record >= leaf > leaflet
Each annotation level only contain annotated elements of the same or lower levels, but not upper level annotations. All data annotation objects can be empty, i.e. do not contain any element. An example JData organization schematic is shown below
super-root{
root1{
group1{
dataset1{
dataset1.1{...}
...
... other auxiliary data ...
}
dataset2{...}
},
group2{
dataset3{...}
}
group3{}
dataset4{...}
... other auxiliary data ...
}
root2[
...
]
}
A data group, dataset or data record can be topologically presented as a branch (including root) or a leaf, but not a leaflet.
All JData keywords are case sensitive. Data groups, datasets and data records can contain metadata to include additional information regarding the data themselves, such as name, create date and user-defined headers. However, metadata can also be present in any branch or leaf.
Below is a short summary of the JData data annotation/storage keywords that can be introduced
- Data grouping:
_DataGroup_,_Dataset_,_DataRecord_ - N-D Array:
_ArrayType_,_ArraySize_,_ArrayIsComplex_,_ArrayIsSparse_,_ArrayData_,_ArrayShape_,_ArrayOrder_,_ArrayZipType_,_ArrayZipSize_,_ArrayZipData_,_ArrayZipEndian_,_ArrayZipLevel_,_ArrayZipOptions_ - Hash/Map:
_MapData_ - Table:
_TableData_,_TableCols_,_TableRows_,_TableRecords_ - Enumeration:
_EnumKey_,_EnumValue_ - Tree:
_TreeData_,_TreeNode_,_TreeChildren_ - Linked List:
_ListNode_,_ListNext_,_ListPrior_,_LinkedList_ - Graph:
_GraphData_,_GraphNodes_,_GraphEdges_,_GraphEdges0_,_GraphMatrix_ - Byte-stream:
_ByteStream_ - Inline metadata:
"item_name::Property1=value1,Property2=value2,...": ... - Metadata record:
{"_DataInfo_":{...}} - Data links and anchors:
_DataLink_,_DataAnchor_
The use of data grouping keywords is not mandatory in a JData document. Nonetheless, properly partitioning and grouping the data based on their semantic relationships and naming the components accordingly can greatly enhance the portability and readability of the data, and thus are strongly recommended.
A data group can be stored in the form of a named node, either
"_DataGroup_": { ... }
or
"_DataGroup_": [ ... ]
A data group annotation can have a name in the below form
"_DataGroup_(a name string): {}
or
"_DataGroup_(a name string): []
If multiple data groups are stored inside a structure, a unique name string, among items under the same parent object, must be provided for each data group. For example
{
"_DataGroup_(name1)":{...},
"_DataGroup_(name2)":{...},
"_DataGroup_(name3)":[...],
...
}
When defining one of the elements of an array object as a data group, one must convert that index node into a structure. For example, in the below array
[
indexed_node1, indexed_node2, indexed_node3,...
]
if one needs to define the indexed_node2 as a data group, one must define
[
indexed_node1,
{
"_DataGroup_(name1)": [index_node2]
},
index_node3,
...
]
where the name "(name1)" is optional.
Similarly, a dataset is specified by
"_Dataset_": { ... }
or
"_Dataset_": [ ... ]
with an optional name parameter in the following format:
"_Dataset_(a name string)": ...
Also similarly, a data record is specified by
"_DataRecord_": { ... }
or
"_DataRecord_": [ ... ]
with an optional name parameter in the following format:
"_DataRecord_(a name string)"
Metadata records can be associated with a data group, dataset or data record (or any branch or leaf). It is optional. Metadata can have two forms
- inline metadata: metadata defined as part of the annotation tag string
- metadata node: a metadata structure added as the first element of a node (named or indexed)
The inline metadata is defined by attaching a series of comma-separated property=value
strings to the data group keywords (_DataGroup_,_Dataset_,_DataRecord_), separated by
two colons, i.e. "::". For example
"_{DataGroup,Dataset,DataRecord}_(name)::Property1=...,Property2=...,..."
The property name should not contain spaces or commas. If commas appear inside the value,
it must be escaped using the form "\,".
Inline metadata can be attached to any named node, for example
"name::Property1=...,Property2=...,..."
One or more permitted white spaces, see above, may be inserted before or after
separators "::", "," and "=" without changing the interpretation of the data.
The inline metadata is primarily used to store simple properties. When storage of more complex metadata is needed, a dedicated "metadata node" should be inserted to the data object as the first child of the annotated object.
For example, if the data to be annotated is a structure, the metadata node is defined
by a named leaf with a specific keyword "_DataInfo_". An example can be
found below:
{
"_DataInfo_": {
"Property1": "...",
"Property2": "...",
"Property3": "...",
...
},
named_node1,
named_node2,
...
}
If the data to be annotated is an array, the metadata record is an indexed leaf as
[
{
"_DataInfo_": {
"Property1": "...",
"Property2": "...",
"Property3": "...",
...
},
}
indexed_node1,
indexed_node2,
...
]
The property names are user-defined. Recommended properties include, but are not limited to, the following list
Version
Author
Comment
UniqueID
CreateTime
ModifiedTime
JData is designed to store a wide variety of data forms. The most common data structures used in the scientific communities include scalars, constants, arrays, structures, tables, associative arrays, trees, and graphs.
The following constants are supported by this version of the specification
NaN: An (integer or floating-point)NaNdefined by the IEEE 754 standard shall be stored as a"_NaN_"string leaflet in text-based JData, unless it is part of an array stored in the annotated N-D array format; in binary JData, it should be stored in the respective (integer or floating-point) IEEE 754 format+/-Inf: A+infinityor-infinitydefined by the IEEE 754 standard shall be stored as string leaflets"+_Inf_"and"-_Inf_", respectively, in the text-based JData ("+" sign can be omitted), unless it is part of an array stored in the annotated N-D array format; in the binary JData, they should be stored in the respective (integer or floating-point) IEEE 754 format- logical
true/false: A logicaltrue/falseshould be represented by the JSONtrue/falselogical values, or[T]/[F]markers in BJData/UBJSON Null: aNull(empty) value shall be stored asnullin JSON and[Z]in BJData/UBJSON
An N-dimensional array is serialized using the row-major format (i.e. the fastest index is the right-most index, similar to arrays in C, C++ or Python; in comparison, MATLAB and FORTRAN arrays are column-majored).
A solid (non-sparse) N-dimensional array can be represented in two forms in JData - the direct storage format and the annotated storage format.
A solid N-D array can be stored directly using JSON/binary-JSON nested array constructs. For example, a 1-D row vector shall be stored as
[1,2,11,9,2.1,10,...]
while a 1-D column-vector shall be stored as
[[1],[2],[11],[9],[2.1],[10],...]
in the JSON-formatted JData.
Below is an example of a 2x3x4 3-D array stored in JSON-formatted JData:
[
[
[1,9,6,0],
[2,9,3,1],
[8,0,9,6]
],
[
[6,4,2,7],
[8,5,1,2],
[3,3,2,6]
]
]
The direct storage format of a solid N-D array does not have the ability to store information regarding the type of the original binary data if using the JSON (text-based) format; however, such information can be stored when using the BJData/UBJSON format.
Please be aware that the BJData format supports an extended syntax compared to UBJSON to facilitate storage and loading of N-D arrays in the binary format. Please refer to the "Grammar" section for details.
In the annotated array storage format, one shall use a structure to store an N-D array. This allows one to use additional fields to store additional information regarding the encoded array.
The annotated array format is shown below for a solid 3x2 array a=[[1,2],[3,4],[5,6]]
{
"_ArrayType_": "typename",
"_ArraySize_": [N1,N2,N3,...],
"_ArrayData_": [1,2,3,4,5,6]
}
Here, the array annotation keywords are defined below:
"_ArrayType_": (required) a case-insensitive string value to specify the type of the data, see below"_ArraySize_": (required) an integer-valued (see below) 1-D row vector, storing the dimensions of the N-D array"_ArrayOrder_": (optional) a case-insensitive string value denoting the array serialization order:"r"or"row"for "row-major" order (as in C/C++/Python) and "c", "col" or "column" for "column-major" order (as in MATLAB/FORTRAN); if missing, JData assumes the row-major order as default."_ArrayData_": (required) a 1-D row vector (or a rectangular array, see below) storing the serialized array values, assuming the row-major element order if"_ArrayOrder_"is not specified.
To facilitate the pre-allocation of the buffer for storage of the array in the parser,
it is required that the "_ArrayType_", "_ArraySize_" and "_ArrayOrder_" (if present)
nodes must appear before "_ArrayData_".
In the case of complex-valued or sparse arrays or the presence of "_ArrayShape_" (see below),
"_ArrayData_" may contain a 2-D rectangular array to store the pre-processed array elements.
One can further serialize such 2-D "_ArrayData_" into a 1-D vector using the row-major order
and then add "_ArrayZipSize_": [Nx, Ny] to store the 2-D "_ArrayData_" dimensions before
1-D serialization.
The supported data types are similar to those supported by the BJData/UBJSON format, i.e.
- uint8: unsigned byte (8-bit),
[U]in BJData/UBJSON - int8: signed byte (8-bit),
[i]in BJData/UBJSON - uint16: unsigned short (16-bit),
[u]in BJData, no correspondence in UBJSON - int16: signed short (16-bit),
[I]in BJData/UBJSON - uint32: unsigned integer (32-bit),
[m]in BJData, no correspondence in UBJSON - int32: signed integer (32-bit),
[l]in BJData/UBJSON - uint64: unsigned long long integer (64-bit),
[M]in BJData, no correspondence in UBJSON - int64: signed long long integer (64-bit),
[L]in UBJSON - half: half-precision floating point (16-bit),
[h]in BJData, no correspondence in UBJSON - single: single-precision floating point (32-bit),
[d]in BJData/UBJSON - double: double-precision floating point (64-bit),
[D]in BJData/UBJSON
The first 8 data types are considered "integer" types, and the last three types are considered "floating-point" types.
In addition, the below two data types can be used as aliases to the uint8 type in an annotated array format:
- char: character arrays (8-bit)
- logical: logical arrays (8-bit)
If the above two aliases are used, a parser may optionally convert the enclosed
uint8 data to character or logical arrays (1-byte).
JData supports the storage of complex values or arrays using only the annotated N-D array storage
format. In this case, a named node "_ArrayIsComplex_":true must be added into the
structure. In the meantime, "_ArrayData_" shall be defined as a 2-D array by concatenating the
serialized real and imaginary parts of the complex array as row vectors, with the real-part
as the first row vector, and the imaginary-part in the 2nd row vector. The two row
vectors must have the same length.
For example, a complex double-precision 1x3 row vector a=[2+6*i, 4+3.2*i, 1.2+9.7*i] shall be stored as
{
"_ArrayType_": "double",
"_ArraySize_": [1, 3],
"_ArrayIsComplex_": true,
"_ArrayData_": [[2, 4, 1.2], [6, 3.2, 9.7]]
}
The "_ArrayIsComplex_" node must be presented before "_ArrayData_".
JData also supports the storage of N-D sparse arrays using the annotated N-D array storage
format. In this case, a named node "_ArrayIsSparse_":true must be added into
the structure. In the meantime, the "_ArrayData_" shall be defined as a 2-D array by
concatenating the N-tuple integer indices (left-most index first, and so on), each as a
row vector, followed by the row vector of the serialized non-zero array element
values. For an N-D sparse array, each non-zero value requires an N-tuple index to specify
its location.
For example, if a 3-D sparse array has the following non-zero elements at the specified
locations by a triplet (i1, i2, i3):
a: i1, i2, i3, value
2 3 1 10.1
3 1 1 9.0
3 3 1 8.1
5 1 2 17
5 2 2 9.4
2 2 3 20.5
it shall be stored in the following JSON format
{
"_ArrayType_": "double",
"_ArraySize_": [5, 4, 3],
"_ArrayIsSparse_": true,
"_ArrayData_": [[2,3,3,5,5,2], [3,1,3,1,2,2], [1,1,1,2,2,3], [10.1,9.0,8.1,17,9.4,20.5]]
}
The "_ArrayIsSparse_" node must be presented before "_ArrayData_".
Using the combination of "_ArrayIsComplex_" and "_ArrayIsSparse_", one can store
a complex-valued sparse array using JData. In this case, both "_ArrayIsComplex_":true
and "_ArrayIsSparse_":true must be presented in the structure, with "_ArrayData_"
ordered by the N-tuple non-zero element indices (left-most index first, and so on),
each as a row vector, followed by a row vector for the real-values of the non-zero elements, and ended
lastly with the row vector for imaginary-values of the non-zero elements. All row vectors
of "_ArrayData_" must have the same length.
For example, if a 3-D sparse array has the following non-zero complex elements at the
specified indices (i1, i2, i3)
a: i1, i2, i3, complex values
2 3 1 10.1+19.0*i
3 1 1 9.0+11*i
3 3 2 8.1+8.2*i
it shall be stored in the following JSON format
{
"_ArrayType_": "double",
"_ArraySize_": [4, 3, 2],
"_ArrayIsComplex_": true,
"_ArrayIsSparse_": true,
"_ArrayData_": [[2,3,3], [3,1,3], [1,1,2], [10.1,9.0,8.1], [19.0,11,8.2]]
}
or the corresponding BJData equivalents.
JData can efficiently store a list of special matrices via the "_ArrayShape_" descriptor.
The "_ArrayShape_" descriptor can be used in conjunction with "_ArrayIsComplex_",
but it shall not be used when "_ArrayIsSparse_" is set to true. The "_ArrayShape_"
node must appear before "_ArrayData_" if present.
The "_ArrayShape_" field shall be either in the form of
"_ArrayShape_": "shapeid"
or
"_ArrayShape_": ["shapeid", param1, param2, ...]
Here, the "shapeid" tag is a case-insensitive string specifying the type of the
special matrix. The currently supported "shapeid" values include
"diag": a diagonal matrix, can be a non-square matrix (optionalparam1defines the length of the diagonal elements, must not be more than the smallest value in the_ArraySize_vector)"upper": an upper triangular (square) matrix (2-D only)"lower": a lower triangular (square) matrix (2-D only)"uppersymm": a symmetric (square) matrix, only storing the upper triangle (2-D only)"lowersymm": a symmetric (square) matrix, only storing the lower triangle (2-D only)
For the above array types, the array entries falling under the mask of the prescribed shape,
referred to as the "effective elements", are serialized as a single vector in the row-major
order in "_ArrayData_". For example, a 3x3 upper triangular matrix:
a11 a12 a13
0 a22 a23
0 0 a33
can be stored as
{
...
"_ArrayShape_": "upper",
"_ArrayData_": [a11, a12, a13, a22, a23, a33]
}
In addition, band matrices are supported via the below "shapeid" tags
"upperband": an upper-band matrix (2-D only, optionalparam1defines the number of super-diagonals of the matrix)"lowerband": a lower-band matrix (2-D only, optionalparam1defines the number of sub-diagonals of the matrix)"uppersymmband": a symmetric band-matrix by storing only the upper-band (2-D only, optionalparam1defines the number of super-diagonals of the matrix)"lowersymmband": a symmetric band-matrix by storing only the lower-band (2-D only, optionalparam1defines the number of sub-diagonals of the matrix)"band": a band matrix (2-D only, optionally, if onlyparam1presents, it defines both the upper and lower bandwidths while if bothparam1andparam2are given, they define the numbers of super-diagonals and sub-diagonals, respectively)
The LAPACK-styled band-matrix storage method is used here. In other words, for the superdiagonal band (the band above the diagonal), the band data are converted into a 2-D matrix with one diagonal line per row (front-padding arbitrary values if the element is outside of the matrix); a sub-diagonal band is converted to a 2-D matrix with one diagonal per row (rear-padding zeros if the element is outside of the matrix).
For example, the below band matrix has 2 super-diagonals, and 1 sub-diagonal, and 1 diagonal (thus, a total bandwidth of 2+1+1=4)
a11 a12 a13 0 0 0
a21 a22 a23 a24 0 0
0 a32 a33 a34 a35 0
0 0 a43 a44 a45 a46
0 0 0 a54 a55 a56
0 0 0 0 a65 a66
the above matrix can be stored as
{
...
"_ArraySize_": [6, 6],
"_ArrayShape_": ["band", 2, 1],
"_ArrayData_": [
[0 0 a13 a24 a35 a46],
[0 a12 a23 a34 a45 a56],
[a11 a22 a33 a44 a55 a66],
[a21 a32 a43 a54 a65 0 ]
]
}
where the "0" entries are elements outside of the matrix and can have an arbitrary
value. The dimensions of the "_ArrayData_" (as a 2-D array) are
(total bandwidth)-by-(maximum array dimension), where
total bandwidth=param1+param2+ 1maximum array dimension= the largest number in the"_ArraySize_"vector
Moreover, Toeplitz matrices are supported via the below "shapeid" value:
"toeplitz": a Toeplitz matrix by storing only the first row and column (padding zeros to have the same length); if the optionalparam1is present , it shall denotemax(#super-diagonals+1, #sub-diagonal+1)
For example, the below non-square (5-by-6) Toeplitz matrix with 3 effective values
in the row [a11 a12 a13] and 2 effective values in the column [a11 a21]
a11 a12 a13 0 0 0
a21 a11 a12 a13 0 0
0 a21 a11 a12 a13 0
0 0 a21 a11 a12 a13
0 0 0 a21 a11 a12
shall be stored as
{
...
"_ArraySize_": [5, 6],
"_ArrayShape_": ["toeplitz", 3],
"_ArrayData_": [
[a11, a12, a13],
[a11, a21, 0]
]
}
Notice that the first element of the row and the column vectors must be identical. The 2nd vector
in "_ArrayData_" is padded in the rear to match the length of the first vector, keeping
"_ArrayData_" in a rectangular shape.
The param1 in the above example is optional, similar to in the band-matrix
cases. If it is not included, the parser shall read the first sub-vector in
"_ArrayData_" to determine the effective element number.
The "_ArrayShape_" descriptor can be used with "_ArrayIsComplex":true to store
complex-valued special matrices. In such cases, the data stored in "_ArrayData_"
shall add an extra left-most dimension (must be 2), and store the real and imaginary
parts of the effective elements in the 1st and 2nd sub-matrices (of matching size),
respectively.
For example, a complex-valued Toeplitz matrix of the same shape shall be stored as
{
...
"_ArraySize_": [5, 6],
"_ArrayIsComplex_": true,
"_ArrayShape_": ["toeplitz", 3],
"_ArrayData_": [
[
[r11, r12, r13],
[r11, r21, 0]
],
[
[i11, i12, i13],
[i11, i21, 0]
]
]
}
where r and i values denote the real and imaginary parts of the effective values
in the Toeplitz matrix.
In addition, when "_ArrayIsComplex":true is present, the "_ArrayShape_" "shapeid"
can take the below additional values to represent "Hermitian matrices"
"upperherm"(similar to"uppersymm")"lowerherm"(similar to"lowersymm")"upperhermband"(similar to"uppersymmband")"lowerhermband"(similar to"lowersymmband")
where the mirrored elements across the diagonal shall have opposite signs for the imaginary parts when decoding the array.
JData supports node-based data compression to enhance the space-efficiency of generated files. The compressed data format is only supported in the annotated array storage format.
For all array types supported in this specification, we want to point out that the
encoded data stored in the "_ArrayData_" container is always rectangular (1-D, 2-D,
or 3-D) in shape. This allows us to efficiently store and parse the data payload and
apply compression. We refer to the data stored in "_ArrayData_" as the
"pre-processed" array.
Four additional nodes are added to the annotated array structure
_ArrayZipType_: (required) a string-valued leaflet to store the compression method, for example, "zlib", "gzip" or "lzma"_ArrayZipSize_: (required) the dimensions of the pre-processed array, i.e. the data originally stored in_ArrayData_in the format specified by"_ArrayType_", before the array binary stream is type-casted to byte stream and compressed._ArrayZipData_: (required) in addition, the"_ArrayData_"node is replaced by"_ArrayZipData_". In the case of JSON-formatted JData files,"_ArrayZipData_"has a string value storing the "Base64" encoded compressed byte-stream of the pre-processed array. In the case of binary flavored JData,"_ArrayZipData_"directly stores the compressed byte stream of the pre-processed array without "Base64" encoding.
In addition, the following optional parameters may also be used
_ArrayZipEndian_: (optional) a case-insensitive string, either "little" or "big", indicating the endianness of the byte-stream before compression; if missing, assumed to be "little"_ArrayZipLevel_: (optional) a numerical value, typically an integer between 0 and 9, specifying the level of the compression (interpretation is method/library-dependent)_ArrayZipOptions_: (optional) an array object allowing users to specify additional compression-method specific parameters (interpretation is method/library-dependent)"_ArrayShuffle_": (optional) if present, must be a non-zero integer; a positive integer specifying the number of bytes at which the serialized raw data buffer is shuffled. For example,"_ArrayShuffle_": 4orders a byte stream[1,2,3,4,5,6,7,8,9,10,11,12]in a new order[1,5,9,2,6,10,3,17,11,4,8,12]; setting"_ArrayShuffle_"to a negative integer specifies bit-wised shuffle with the absolute value as the bit-wised shuffle spacing."_ArrayShuffle_"must be applied, if present, before compression/encoding and an unshuffle operation must be applied upon decompression/decoding.
When a compressed array format is used, "_ArrayZipType_" and
"_ArrayZipSize_" must appear before "_ArrayZipData_".
"_ArrayZipData_" and "_ArrayData_" can not appear under the same parent node.
In a map or an associative/hashed array, the element can be accessed using a unique
key. For example, the below pseudo code defines a 3-element associative array @a with
3 unique keys
@a = {'Andy'->21, 'William'->21, 'Om'->22}
Such data structure can be conveniently represented using JSON/binary JSON as
{
"Andy": 21,
"William": 21,
"Om": 22
}
To store maps with non-string valued keys, one can use a 2-D array enclosed inside
a "_MapData_" construct, for example
"_MapData_": [
["Andy", 21],
["William",21],
["Om", 22],
[120, 30],
[2.9, 45]
]
Each element of the _MapData_ array may contain more than 2 elements; the additional
elements shall be treated as auxiliary data.
A table data structure is defined by a 2-dimensional grid of data indexed by the table columns (fields) and rows (records). For example
Name Age Degree Height
---- ------- ------ ------
Andy 21 BS 69.2
William 21 MS 71.0
Om 22 BE 67.1
Such data can also be stored in JSON/binary JSON using 3 dedicated keywords
"_TableCols_": a 1-D array denoting the name and type for each of the column of the table, can be empty if no name or types are associated with the columns"_TableRows_": a 1-D array denoting the name and type for each of the row of the table can be empty if no name or types are associated with the rows"_TableRecords_": a 2-D array storing each entry in the 2-D table.
For example, the above table can be serialized using the below format
{
"_TableCols_": ["Name", "Age", "Degree", "Height"],
"_TableRows_": [],
"_TableRecords_": [
["Andy", 21, "BS", 69.2],
["William", 21, "MS", 71.0],
["Om", 22, "BS", 67.1]
]
}
The elements in the "_TableRows_" and "_TableCols_" can be a structure to accommodate
additional metadata associated with the row or column. One can use "DataName" to specify
the string name of the row or column, and "DataType" to specify their data types.
The supported "DataType" values are the same as those defined for "_ArrayType_" for
numerical arrays, with the addition of the below types
"string": a UTF-8 encoded string"bool": a boolean: eithertrueorfalse"blob": a binary byte-stream, the format of theblobrecord is described in the Generic byte-stream data below (without the"_ByteStream_"container)
For example, in the above example, one can define the columns as
"_TableCols_": [
{"DataName": "Name", "DataType": "string"},
{"DataName": "Age", "DataType": "uint32"},
{"DataName": "Degree", "DataType": "string"},
{"DataName": "Height", "DataType": "single"}
],
...
In addition, a table can also be converted to an array of maps if a name is associated with each of the rows or columns.
- Array of structures: the table data can be serialized by grouping records in rows, such as
[
{"Name": "Andy", "Age": 21, "Degree": "BS", "Height": 69.2},
{"Name": "William","Age": 21, "Degree": "MS", "Height": 71.0},
{"Name": "Om", "Age": 22, "Degree": "BE", "Height": 67.1}
]
- Structure of arrays: the table data can also be organized in columns, where often contains the same data types. Using the same above example, one can write the following JData form
{
"Name": ["Andy","William","Om"],
"Age": [21,21,22],
"Degree": ["BS","MS","BE"],
"Height": [69.2,71.0,67.1]
}
The choice of forms is user-dependent. Typically, the "structure of arrays" form leads to a smaller file size.
The above JData table can be enclosed inside an optional field "_TableData_(table_name)":{...}
(if inside a structure) or {"_TableData_":{...}} (if inside an array) to inform the
parser about the start of the data structure.
An enumeration data structure is spatially effective when storing array data containing repeated values. For example, the following array
"gender": ["M","M","F","F","F","M",...,"F"]
contains only two possible values, "M" or "F", each repeating many times. We use the following
keywords to represent such data:
"_EnumKey_": this is an array containing unique values that appear in an array; the unique values can have mixed data types."_EnumValue_": this is an integer array with each entry denoting the index (starting from 1) of the value appearing in"_EnumKey_";"_EnumValue_"can be an N-D array.
With the above construct, we can store the above "gender" example as
"gender": {
"_EnumKey_": ["M","F"],
"_EnumValue_": [1,1,2,2,2,1,...,2]
}
Because "_EnumValue_" is an integer array, one can further use the N-D array annotation format to
compress the data and reduce the storage overhead.
A tree-like data structure can be conveniently represented by JSON/binary-JSON formatted files due to the similar underlying structures. In JData, we use two keywords to encapsulate a tree-like data structure.
The tree-node name and the associated data are stored using a named node with a keyword
_TreeNode_:
"_TreeNode_(node_name)": node_data
If a tree-node contains children, we use a named array to store the children data:
"_TreeChildren_":[
{"_TreeNode_(child1)": data1},
{"_TreeNode_(child1)": data2},
...
]
For example, a tree data structure
root(data0)
├──── node1(data1)
├──── node2(data2)
│ ├──── node2.1(data2.1)
│ └──── node2.2(data2.2)
└──── node3(data3)
can be represented by the below JSON structure
{
"_TreeNode_(root)": data0,
"_TreeChildren_": [
{"_TreeNode_(node1)": data1},
{
"_TreeNode_(node2)": data2,
"_TreeChildren_": [
{"_TreeNode_(node2.1): data2.1},
{"_TreeNode_(node2.2): data2.2}
]
},
{"_TreeNode_(node3)": data3}
]
}
and the corresponding binary JSON equivalents. The notations "data0", "data1", etc. are parsed node data in JData format according to the rules defined in this section and depend on the type of the data.
One can add either inline or dedicated metadata records to store additional information about the tree nodes, for example
{
"_TreeNode_(root)::NodeID=1,ParentID=0,Path=#root": data0,
"_TreeChildren_": [
{"_TreeNode_(node1)::NodeID=2,ParentID=1,Path=#root#node1": data1},
{
"_TreeNode_(node2)::NodeID=3,ParentID=1": data2,
"_TreeChildren_": [
{"_TreeNode_(node2.1)::NodeID=4,ParentID=3": data2.1},
{"_TreeNode_(node2.2)::NodeID=5,ParentID=3": data2.2}
]
},
{"_TreeNode_(node3)::NodeID=6,ParentID=1": data3}
]
}
Auxiliary data can be inserted to different levels of the above JData tree document
as named nodes as long as the name of the auxiliary node does not conflict with
the "_TreeNode_(name)" and "_TreeChildren_" of the same level. Behaviors for parsing
the auxiliary data are application dependent.
The above JData tree can be enclosed inside an optional field "_TreeData_(tree_name)":{...}
(if inside a structure) or {"_TreeData_":{...}} (if inside an array) to inform the
parser of the start of the data structure.
Similar to the storage of trees, we use additional keywords to encapsulate a singly or doubly linked list inside a JSON/binary-JSON array construct. The relevant keywords are
"_ListNode_(unique_name)": node_data
and
"_ListNext_": "unique_name_of_next_node"
"_ListPrior_": "unique_name_of_prior_node"
For example, the below linked list
head(data0)->node1(data1)->node2(data2)->node3(data3)
shall be stored as
[
{
"_ListNode_(head)": data0,
"_ListNext_": "node1"
},
{
"_ListNode_(node1)": data1,
"_ListNext_": "node2"
},
{
"_ListNode_(node2)": data2,
"_ListNext_": "node3"
},
{
"_ListNode_(node3)": data3,
"_ListNext_": null
}
]
If a node does not have a next or prior element, the "_LinkNext_" or "_LinkPrior_" value
should be set to null.
The name label referred in the "_LinkNext_" or "_LinkPrior_" fields has a scope limited to
the current list, i.e. the parent array one-level above the node. Multiple linked lists can
share the same names if they are stored in a parallel or nested fashion.
The above linked list can be enclosed inside an optional field, "_LinkedList_(list_name)":[...]
(if inside a structure) or {"_LinkedList_":[...]} (if inside an array), to inform the parser of
the start of the data structure.
In JData, we use the below keywords to encapsulate a graph data structure
"_GraphNodes_": a JData array object to store the serialized node data"_GraphEdges_": a JData array object to store the connections between nodes. Each edge is represented by a 2 or 3-element array["start node name", "end node name", (optional) edge data].
For example, if we modify the above linked list by adding a directed edge from "node1" to
"node3" and from "node3" to "node2", such as the data suggested by this diagram
┌--------------------------┐
│ V
head(data0)->node1(data1)->node2(data2)->node3(data3)
^ │
└-------------┘
we can then store this data structure as
{
"_GraphNodes_":{
"head": data0,
"node1": data1,
"node2": data2,
"node3": data3
},
"_GraphEdges_":[
["head", "node1", edgedata01],
["node1", "node2", edgedata12],
["node1", "node3", edgedata13],
["node2", "node3", edgedata23],
["node3", "node2", edgedata32]
]
}
The data associated with each edge (edgedata) in this example is optional and can be any JData structure supported in this document. If the edge data is a scalar, it can be interpreted as weights in a weighted graph.
By default, the graph is assumed to be a directed graph. If a user intends to store undirected
graphs using the above format, one must use "_GraphEdges0_" or "_GraphEdges0_" to
enclose the edge data.
The above graph data can be enclosed inside an optional field, "_GraphData_(graph_name)_":{...}
(if inside a structure) or {"_GraphData_":{...}} (if inside an array), to inform the parser of the
start of the data structure.
When no data or metadata are associated with the edges, one can also use the graph's adjacency
matrix to represent the connections between graph nodes. The adjacency matrix must only have 0
and 1 values, with a 1 at A(i,j) indicating a directed edge from the i-th node in the
_GraphNode_ list to the j-th node in that list and a 0 indicating no connection.
For example, the adjacency matrix of the above graph can be written as
head node1 node2 node3
head 0 1 0 0
node1 0 0 1 1
node2 0 0 0 1
node3 0 0 1 0
In this case, instead of using _GraphEdges_, we use _GraphMatrix_ to store the
adjacency matrix using the N-D array syntax above. For example, for the same graph,
we can write this matrix using the direct form
{
"_GraphNodes_":{
"head": data0,
"node1": data1,
"node2": data2,
"node3": data3
},
"_GraphMatrix_":[
[0,1,0,0],
[0,0,1,1],
[0,0,0,1],
[0,0,1,0]
]
}
Notice that the matrix is serialized in the row-major order in the above example. Alternatively, one can also use the annotated array format to take advantage of the sparsity of the data:
{
"_GraphNodes_":{
"head": data0,
"node1": data1,
"node2": data2,
"node3": data3
},
"_GraphMatrix_":{
"_ArrayTye_": "uint8",
"_ArraySize": [4,4],
"_ArrayIsSparse": true,
"_ArrayData_": [1,2,2,3,4, 2,3,4,4,2, 1,1,1,1,1]
}
}
One can also apply array compression, as explained above, to further reduce the file size
{
...
"_GraphMatrix_": {
"_ArrayType_": "uint8",
"_ArraySize_": [4,4],
"_ArrayZipSize_": [1,16],
"_ArrayZipType_": "zlib",
"_ArrayZipEndian_": "little",
"_ArrayZipData_": "eJxjYGQAAkYQyQhCAAA5AAY=="
}
}
here the "_ArrayZipData_" stores the row-major-serialized, byte-typecasted,
zlib-compressed and finally base64-encoded (if stored in JSON, for binary JSON,
base64 is not needed) adjacency matrix.
In addition, a weighted graph can also be stored using the adjacency matrix by replacing the "1"s in the matrix by the weight values (a numerical scalar).
A binary byte-stream of arbitrary length can be stored in the text-based JData using the below construct
"_ByteStream_":"..."
where the string value "..." shall be the Base64-encoded byte-stream binary data. For example, a string
valued byte-stream "JData specification" shall be stored as
"_ByteStream_":"SkRhdGEgc3BlY2lmaWNhdGlvbg=="
In the binary JData, a byte-stream shall be encoded using a similar "name":value pair where the
value is represented by an [H] marker. As specified in the
BJData Specification (Draft 2),
the [H] marker is immediately followed by the length of the byte stream, then followed by
the raw binary values of the byte-stream without Base64 encoding. The same example above
can be stored as
[U][12][_ByteStream_][H][U][19][JData specification]
The parsing and interpretation of the byte-stream data are application dependent. This is the most
generic form of data storage but contains the least data semantic information. One can use this
construct as a container to binary files, data segments, or encrypted data or files. In the case
of data encryption, additional information related to the encryption and decryption, if needed,
shall be stored as metadata to the "_ByteStream_" object with a format specified in the
Metadata section.
An example _ByteStream_ record that includes additional information in the metadata section is
shown below:
"_ByteStream_":{
"_DataInfo_":
"MediaType": "text/plain",
"Encoding": ["zlib","base64"],
"ByteLength": 1250,
},
"Data": "..."
}
where MediaType specifies the original data MIME type - in a format compatible to those
used in common Internet standards (such as RFC2045, RFC2046 etc), additional
encoding steps (specified by "Encoding"), in the order or processing, that converts the
original data to the byte stream stored in the "Data" subfield, data length etc.
For example, in the above _ByteStream_ object, the "Data" subfield stores a plain-text
object, by first apply a zlib-based compression to the text stream, and then apply "base64"
encoding. The total converted "Data" byte length is 1250-byte, as shown in ByteLength.
A JData-compliant parser should support the below programming interfaces for accessing and processing JData files
JD_GetNode(root_node, index_vector, is_compact): returns the JData node pointed by the index vector, reference from theroot_nodeJD_GetName(node): returns the full name string of the specified nodeJD_GetData(node): returns the value associated with the specified nodeJD_GetType(node): returns the node typeJD_GetLength(node): returns the number of children of the specified node
Essentially, JData stores a serialized version of complex data using collections of sequential or nested nodes, either in the named or indexed form. To access any element (a leaflet, leaf or branch) of the JData document, one should use a vector of indices that points to the specific node.
A JData-compliant parser must be able to retrieve JData elements via the below pseudo-code interface using a linear index vector
JD_Node item = JD_GetNode(JD_Node root, [i1, i2, i3, i4, ...], is_compact)
where i1 is the index of the data on the top-most level (relative to the root level of the
"root" object), i2, is the index along the 2nd level, and so on. Each index is an
integer, starting from 1, denoting the order
of the data element among the serialized elements of the same level. In order words, if
the current level is an array object, the index is the count of the elements before this data
element plus 1; if the current level is a structure, the index is the count of the named
nodes appearing before this data plus 1. Using the tree data structure above, the linear index
of each node is listed below on the right side:
{
"_TreeNode_(root)": data0, <- [1]
"_TreeChildren_": [ <- [2]
{"_TreeNode_(node1)": data1}, <- [2,1]
{ <- [2,2]
"_TreeNode_(node2)": data2, <- [2,2,1]
"_TreeChildren_": [ <- [2,2,2]
{"_TreeNode_(node2.1): data2.1}, <- [2,2,2,1]
{"_TreeNode_(node2.2): data2.2} <- [2,2,2,2]
]
},
{ <- [2,3]
"_TreeNode_(node3)": data3 <- [2,3,1], or [[2,3]]
}
]
}
One can insert zeros to the right-side of the indexing vector if the array storing the vector
has a length longer than the depth of the assessed node. In this case, the first 0 scanning
from left to right of the indexing vector is considered the termination flag of the index.
In other words, index vectors [2,2], [2,2,0] and [2,2,0,0] are equivalent.
The third parameter, is_compact, is a boolean flag. If set to true, JD_GetNode
shall skip the index if any of the dimensions along the indexing vector is a singlet,
i.e. the child count is 1. The compact indexing vector, enclosed by double-square-brackets
as [[...]], shall be passed to JD_GetNode as the 2nd input when is_compact is true.
Using the above example, both index vectors [[2,3]] and [2,3,1] refer to
"_TreeNode_(node3)": data3. Please be aware that the compact indexing vector can not
distinguish between row and column vectors as the column vector in JData has a trailing
singlet dimension (see N-D array section).
An optional alternative indexing vector definition allows the replacement of the index within a
structure by a corresponding string, which can be the name of the data item or a hashed version of it.
For example, the item {"_TreeNode_(node2.1): data2.1} in the above may also be accessed
via ["_TreeChildren_",2,"_TreeChildren_",1]. This alternative indexing scheme is less
sensitive to data serialization orders, but requires the parser to handle both string
and integer inputs.
The JD_GetNode API may also optionally provide supports to the widely used
JSON Path Notations to query individual elements
in a JData/JSON based data structure.
For each JData item identified via an indexing vector, a JData-compliant library must be able to
retrieve the "name" and "data" properties of the object via the below pseudo-code interface
string name=JD_GetName(JDataNode item)
JD_Node data=JD_GetData(JDataNode item)
here "name" is a string variable recording the full item name, including the inline metadata
if present; "data" is the "value" of the object. When the inquired data object is an element
in an array, the returned name must be empty.
A JData-compliant parser should also allow users to retrieve the type and the children count of the node via the below interfaces
JD_NodeType type = JD_GetType(JD_Node item)
The type must be able to distinguish the below 3 basic types
- a leaflet,
- a structure,
- an array
Combined with the JD_GetName, one can also query if the element is a named or indexed one.
Additionally, a JData-compliant library must provide the below interface to obtain the count of the children for each data item. A leaflet, an empty structure or an empty array should return a length of 0.
integer length = JD_GetLength(JD_Node item)
One can choose either the text or binary format to save the raw data into a JData file, with the former following JSON storage requirements and the latter following BJData/BJSON storage requirements. This specification permits both lossless and lossy conversions between the raw data to JSON, raw data to BJData/UBJSON, and JSON and BJData/UBJSON files.
- lossless conversion: the input data are preserved after a save-load round-trip conversion to and from one of the JData formats; type-casting from low precision to high precision types is permitted
- lossy conversion: the input data may lose precision during the save-load round-trip conversion but the loss of precision shall be limited to a level that is tolerable by the application.
For best practices, the use of lossless conversion is highly recommended. Here are some general recommendations to best preserve data precision:
- Use binary-based JData to retain data type and binary information
- When using text-based JData, make sure to print sufficient decimals for floating-point numerical data (typically 7 for single-precision and 17 for double-precision values) to retain the full precision
- When saving numerical arrays to text-based JData, consider using annotated array storage format, either with or without data compression, to store binary type information
- When parsing numerical arrays stored in the direct storage format in text-based JSON, one should consider using double-precision as the read-buffer to avoid truncation of input data and loss of precision.
In addition, if a transformation of the data does not alter the (full or compact) indexing vector of all leaflets in a JData document, it is referred to as an "isometric transform" and is permitted. An example of an isometric transform is the conversion from a structure to an array as shown in the below example:
{
"a": {
"name1": value1, <- [1,1] or ["a","name1"]
"name2": value2, <- [1,2] or ["a","name2"]
"name3": value3 <- [1,3] or ["a","name3"]
},
"b": "value4" <- [2] or ["b"]
}
to
{
"a": [
{
"name1": value1 <- [[1,1]] or [["a","name1"]]
},
{
"name2": value2 <- [[1,2]] or [["a","name2"]]
},
{
"name3": value3 <- [[1,3]] or [["a","name3"]]
}
],
"b": "value4" <- [2] or ["b"]
}
The only permitted "non-isometric transform" is the conversion between a direct N-D array to an annotated N-D array.
JData files support referencing and internal/external linking via the definition of data links and anchors.
A link is defined by a named leaflet or leaf as shown in the below two styles
"link_style1": {
"_DataLink_": "path"
},
"link_style2": {
"_DataLink_": {
"URI": "path",
"Parameters": [...],
"MaxRecursion": 1
}
}
The "path" string specifies the Uniform Resource Identifier (URI) of the referenced
JData data document using a format compliant to the [RFC3986] specification, followed
by the indexing vector string to point to a specific element of the referenced document.
For example, the below link
{
"_DataLink_": "file:///space/test/jdfiles/tree.jdat:[1,2,2]"
}
asks the parser to read a local file located at "/space/test/jdfiles/tree.jdat" and
load the node specified by indexing vector [1,2,2], starting from the root (or super-root
if containing CJSON) to replace the "_DataLink_" node in the current document.
If using a "_DataLink_" structure, additional parameters can be specified via
user-defined parameters, such as "Parameters" and "MaxRecursion" to fine-tune
the linking behavior.
For easy referencing, JData permits the definitions of named anchors inside the "metadata" section for each node using the following format
"obj::_DataAnchor_=a_unique_anchor_name": ...
or
"obj": {
"_DataInfo_"{
...
"_DataAnchor_": "a_unique_anchor_name",
...
}
...
}
Then, the data object can be referenced as shown in the below example "global_link1"
"global_link1": {
"_DataLink_": "https://example.com:8080/space/test/jdfiles/tree.jdat#a_unique_anchor_name"
},
"local_link1": {
"_DataLink_": "#a_unique_anchor_name"
}
"local_link2": {
"_DataLink_": [1,2,2]
}
"local_compact_link3": {
"_DataLink_": [[1,2,2]]
}
A data link URI starting with "#" refers to the data anchor defined within the same document,
such as shown in the "local_link1" example above. Similarly, one can directly use the
indexing vector, in the form of an array, in the value field of "_DataLink_" to cite
a local node, as shown in the "local_link2" example above. A compact indexing vector
(eliminating singlet dimensions) can be represented by a pair of double-square-brackets,
i.e. 1-level nested vector, as shown in the "local_compact_link3" example above. The
behaviors of other types of data link values are not specified.
Instead of using the above described index vector to reference a sub-element inside an externally linked JData file, this specification also permit the use of JSONPath specifiers. JSONPath uses a series of dot-separated names to locate an element inside a JSON data tree, such as
$.name1.name2.name3[0].name4
in this notation, $ refers to the root of the JSON object, $.name1 refers to the root-level
sub-element name1; $.name1.name2.name3[0] specifies the name2 child of $.name1 has a child
named .name3, which is an array; using .name3[0] means taking the first element of the
array. Lastly, the first element of the name3 array has a child named .name4.
When the object names contain . [, or ], they must be escaped by inserting a
\ before. For example, $.file.test\.json specifies a key named test.json under the $.file object.
It is worth mentioning that JSONPath supports deep-scan (..) and filtering operators,
although this specification does not require the parsers to fully support all JSONPath
operators.
For the text-based JData file, the recommended file suffix is ".jdt"; for
the binary-JSON based JData file, the recommended file suffix is ".jdb".
The MIME type for the text-based JData document is
"application/jdata-text"; that for the binary JData document is
"application/jdata-binary"
The main appeals of JSON and binary-JSON are their simplicity and portability, which are often missing from other alternatives. In this document, we aim to extend the ability of JSON/binary-JSON to store and interchange complex data structures without needing to modify the language syntax, making the generated JData files readily usable for most existing JSON/binary-JSON encoders and decoders.
Specifically, we defined JSON/binary-JSON-based constructs to store N-D arrays, tables, trees, linked lists, and graphs, and added the ability to associate metadata to any element of the JData document. In addition, we also defined a set of core library interfaces to query and access the values and properties of the data units stored in a JData document. To enhance space-efficiency and flexibility, we also introduced array data compression and data linking/referencing mechanisms.
Although JData does not have all the sophisticated features of other advanced binary data exchange formats, such as HDF5, it is well suited for the storage of small to medium sized datasets generated in many scientific domains or IT applications. Combined with the excellent availability of parsers and web-friendliness, JData is expected to be easily adoptable and extended in the future.